import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):

	layer_name = 'layer%s'%n_layer
	with tf.name_scope('layer'):
		with tf.name_scope('weights'):
			Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
			tf.summary.histogram(layer_name+'/weights',Weights)
		with tf.name_scope('biases'):
			biases = tf.Variable(tf.zeros([1,out_size])+ 0.1,name='b')
			tf.summary.histogram(layer_name+'/biases',biases)
		with tf.name_scope('Wx_plus_b'):
			Wx_plus_b=tf.add(tf.matmul(inputs, Weights), biases)
		if activation_function is None:
			outputs=Wx_plus_b
		else:
			outputs = activation_function(Wx_plus_b)
			tf.summary.histogram(layer_name+'/outputs',outputs)
		return outputs


x_data=np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)
y_data=np.square(x_data)-0.5 + noise


with tf.name_scope('inputs'):
	xs = tf.placeholder(tf.float32,[None,1],name='x_input')
	ys = tf.placeholder(tf.float32,[None,1],name='y_input')

l1=add_layer(xs, 1, 10,n_layer=1,activation_function=tf.nn.relu)
predition = add_layer(l1, 10, 1,n_layer=2,activation_function=None)

with tf.name_scope('loss'):
	loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - predition),reduction_indices=[1]))
	tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
	train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()

merged = tf.summary.merge_all()

writer=tf.summary.FileWriter("logs/",sess.graph)



sess.run(init)



# fig=plt.figure()
# ax=fig.add_subplot(1,1,1)
# ax.scatter(x_data,y_data)
# plt.ion()
# plt.show()



# for i in range(100):
# 	sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
# 	if i % 50 == 0:
# 		#print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
# 		try:
# 			ax.lines.remove(lines[0])
# 		except Exception:
# 			pass
# 		predition_value = sess.run(predition,feed_dict={xs:x_data,ys:y_data})
# 		lines = ax.plot(x_data,predition_value,'r-',lw=5)
# 		plt.pause(0.1)



for i in range(100):
	sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
	if i % 50 == 0:
		result=sess.run(merged,feed_dict={xs:x_data,ys:y_data})
		writer.add_summary(result,i)